Exploratory_Data_Analysis_Terrorism¶

In this task, we will be performing exploratory data analysis on the dataset "GlobalTerrorism" and try to find out the hot zone of terrorism. Also, we will derive the security issues and various insights.

In [1]:
# Importing all the libraries needed in this notebook
import math
import warnings
import numpy as np 
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import plotly.offline as py
import plotly.graph_objs as go
import matplotlib.pyplot as plt
warnings.filterwarnings('ignore')

Loading and Reading The Data Set¶

In [2]:
global_terror = pd.read_csv('globalterrorismdb_0718dist.csv',encoding='ISO-8859-1')
In [3]:
global_terror.head() # first five values of the dataset
Out[3]:
eventid iyear imonth iday approxdate extended resolution country country_txt region ... addnotes scite1 scite2 scite3 dbsource INT_LOG INT_IDEO INT_MISC INT_ANY related
0 1.970000e+11 1970 7 2 NaN 0 NaN 58 Dominican Republic 2 ... NaN NaN NaN NaN PGIS 0 0 0 0 NaN
1 1.970000e+11 1970 0 0 NaN 0 NaN 130 Mexico 1 ... NaN NaN NaN NaN PGIS 0 1 1 1 NaN
2 1.970010e+11 1970 1 0 NaN 0 NaN 160 Philippines 5 ... NaN NaN NaN NaN PGIS -9 -9 1 1 NaN
3 1.970010e+11 1970 1 0 NaN 0 NaN 78 Greece 8 ... NaN NaN NaN NaN PGIS -9 -9 1 1 NaN
4 1.970010e+11 1970 1 0 NaN 0 NaN 101 Japan 4 ... NaN NaN NaN NaN PGIS -9 -9 1 1 NaN

5 rows × 135 columns

In [4]:
global_terror.columns
Out[4]:
Index(['eventid', 'iyear', 'imonth', 'iday', 'approxdate', 'extended',
       'resolution', 'country', 'country_txt', 'region',
       ...
       'addnotes', 'scite1', 'scite2', 'scite3', 'dbsource', 'INT_LOG',
       'INT_IDEO', 'INT_MISC', 'INT_ANY', 'related'],
      dtype='object', length=135)
In [5]:
global_terror.rename(columns={'iyear':'Year','imonth':'Month','iday':'Day','country_txt':'Country','provstate':'state',
                       'region_txt':'Region','attacktype1_txt':'AttackType','target1':'Target','nkill':'Killed',
                       'nwound':'Wounded','summary':'Summary','gname':'Group','targtype1_txt':'Target_type',
                       'weaptype1_txt':'Weapon_type','motive':'Motive'},inplace=True)

Dropping out irrelevant columns¶

In [6]:
# Important data for further processing
global_terror=global_terror[['Year','Month','Day','Country','state','Region','city','latitude','longitude','AttackType','Killed',
               'Wounded','Target','Summary','Group','Target_type','Weapon_type','Motive']]
In [7]:
global_terror.head()
Out[7]:
Year Month Day Country state Region city latitude longitude AttackType Killed Wounded Target Summary Group Target_type Weapon_type Motive
0 1970 7 2 Dominican Republic NaN Central America & Caribbean Santo Domingo 18.456792 -69.951164 Assassination 1.0 0.0 Julio Guzman NaN MANO-D Private Citizens & Property Unknown NaN
1 1970 0 0 Mexico Federal North America Mexico city 19.371887 -99.086624 Hostage Taking (Kidnapping) 0.0 0.0 Nadine Chaval, daughter NaN 23rd of September Communist League Government (Diplomatic) Unknown NaN
2 1970 1 0 Philippines Tarlac Southeast Asia Unknown 15.478598 120.599741 Assassination 1.0 0.0 Employee NaN Unknown Journalists & Media Unknown NaN
3 1970 1 0 Greece Attica Western Europe Athens 37.997490 23.762728 Bombing/Explosion NaN NaN U.S. Embassy NaN Unknown Government (Diplomatic) Explosives NaN
4 1970 1 0 Japan Fukouka East Asia Fukouka 33.580412 130.396361 Facility/Infrastructure Attack NaN NaN U.S. Consulate NaN Unknown Government (Diplomatic) Incendiary NaN
In [8]:
# Checking for the null values 
global_terror.isnull().sum()
Out[8]:
Year                0
Month               0
Day                 0
Country             0
state             421
Region              0
city              434
latitude         4556
longitude        4557
AttackType          0
Killed          10313
Wounded         16311
Target            636
Summary         66129
Group               0
Target_type         0
Weapon_type         0
Motive         131130
dtype: int64

Checking the dataset's information¶

In [9]:
global_terror.info() # Returns the concise summary

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 181691 entries, 0 to 181690
Data columns (total 18 columns):
 #   Column       Non-Null Count   Dtype  
---  ------       --------------   -----  
 0   Year         181691 non-null  int64  
 1   Month        181691 non-null  int64  
 2   Day          181691 non-null  int64  
 3   Country      181691 non-null  object 
 4   state        181270 non-null  object 
 5   Region       181691 non-null  object 
 6   city         181257 non-null  object 
 7   latitude     177135 non-null  float64
 8   longitude    177134 non-null  float64
 9   AttackType   181691 non-null  object 
 10  Killed       171378 non-null  float64
 11  Wounded      165380 non-null  float64
 12  Target       181055 non-null  object 
 13  Summary      115562 non-null  object 
 14  Group        181691 non-null  object 
 15  Target_type  181691 non-null  object 
 16  Weapon_type  181691 non-null  object 
 17  Motive       50561 non-null   object 
dtypes: float64(4), int64(3), object(11)
memory usage: 25.0+ MB

Data Visualization¶

Destructive Features¶

In [10]:
print("Country with the most attacks:",global_terror['Country'].value_counts().idxmax())
print("City with the most attacks:",global_terror['city'].value_counts().index[1]) #as first entry is 'unknown'
print("Region with the most attacks:",global_terror['Region'].value_counts().idxmax())
print("Year with the most attacks:",global_terror['Year'].value_counts().idxmax())
print("Month with the most attacks:",global_terror['Month'].value_counts().idxmax())
print("Group with the most attacks:",global_terror['Group'].value_counts().index[1])
print("Most Attack Types:",global_terror['AttackType'].value_counts().idxmax())

Country with the most attacks: Iraq
City with the most attacks: Baghdad
Region with the most attacks: Middle East & North Africa
Year with the most attacks: 2014
Month with the most attacks: 5
Group with the most attacks: Taliban
Most Attack Types: Bombing/Explosion
In [13]:
from wordcloud import WordCloud
from scipy import signal
cities = global_terror.state.dropna(False)
plt.subplots(figsize=(20,10))
wordcloud = WordCloud(background_color = 'black',
                     width = 500,
                     height = 400).generate(' '.join(cities))
plt.axis('off')
plt.imshow(wordcloud)
plt.show()
In [14]:
global_terror['Year'].value_counts(dropna = False).sort_index()
Out[14]:
1970      651
1971      471
1972      568
1973      473
1974      581
1975      740
1976      923
1977     1319
1978     1526
1979     2662
1980     2662
1981     2586
1982     2544
1983     2870
1984     3495
1985     2915
1986     2860
1987     3183
1988     3721
1989     4324
1990     3887
1991     4683
1992     5071
1994     3456
1995     3081
1996     3058
1997     3197
1998      934
1999     1395
2000     1814
2001     1906
2002     1333
2003     1278
2004     1166
2005     2017
2006     2758
2007     3242
2008     4805
2009     4721
2010     4826
2011     5076
2012     8522
2013    12036
2014    16903
2015    14965
2016    13587
2017    10900
Name: Year, dtype: int64

Terrorist Activities Each Year¶

In [15]:
x_year = global_terror['Year'].unique()
y_count_years = global_terror['Year'].value_counts(dropna = False).sort_index()
plt.figure(figsize = (18,10))
sns.barplot(x = x_year,
           y = y_count_years,
           palette = 'rocket')
plt.xticks(rotation = 50)
plt.xlabel('Attack Year')
plt.ylabel('Number of Attacks Each Year')
plt.title('Attack In Years')
plt.show()
In [18]:
plt.subplots(figsize=(20,10))
sns.countplot(x='Year', data=global_terror, palette='RdYlGn_r', edgecolor=sns.color_palette("YlOrBr", 10))
plt.xticks(rotation=50)
plt.title('Number Of Terrorist Activities Each Year')
plt.show()

Terrorist Activities By Region In Each Year¶

In [19]:
pd.crosstab(global_terror.Year, global_terror.Region).plot(kind='area',figsize=(20,10))
plt.title('Terrorist Activities By Region In Each Year')
plt.ylabel('Number of Attacks')
plt.show()
In [20]:
global_terror['Wounded'] = global_terror['Wounded'].fillna(0).astype(int)
global_terror['Killed'] = global_terror['Killed'].fillna(0).astype(int)
global_terror['Casualities'] = global_terror['Killed'] + global_terror['Wounded']
In [21]:
# Top 50 worst terrorist attacks
global_terror1 = global_terror.sort_values(by='Casualities',ascending=False)[:50]
In [22]:
heat=global_terror1.pivot_table(index='Country',columns='Year',values='Casualities')
heat.fillna(0,inplace=True)
In [23]:
heat.head()
Out[23]:
Year 1982 1984 1987 1992 1994 1995 1996 1997 1998 2001 ... 2005 2006 2007 2008 2009 2013 2014 2015 2016 2017
Country
Afghanistan 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 536.0 0.0 584.0
Algeria 0.0 0.0 0.0 0.0 0.0 0.0 0.0 450.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Chad 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 1161.0 0.0 0.0 0.0 0.0 0.0 0.0
Egypt 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 438.0
Ethiopia 0.0 0.0 0.0 500.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

5 rows × 21 columns

In [24]:
import plotly.offline as py
py.init_notebook_mode(connected=True)
import plotly.graph_objs as go
colorscale = [[0, '#edf8fb'], [.3, '#00BFFF'],  [.6, '#8856a7'],  [1, '#810f7c']]
heatmap = go.Heatmap(z=heat.values, x=heat.columns, y=heat.index, colorscale=colorscale)
data = [heatmap]
layout = go.Layout(
    title='Top 50 Worst Terror Attacks in History from 1982 to 2017',
    xaxis = dict(ticks='', nticks=20),
    yaxis = dict(ticks='')
)
fig = go.Figure(data=data, layout=layout)
py.iplot(fig, filename='heatmap',show_link=False)
In [25]:
global_terror.Country.value_counts()[:21]
Out[25]:
Iraq              24636
Pakistan          14368
Afghanistan       12731
India             11960
Colombia           8306
Philippines        6908
Peru               6096
El Salvador        5320
United Kingdom     5235
Turkey             4292
Somalia            4142
Nigeria            3907
Thailand           3849
Yemen              3347
Spain              3249
Sri Lanka          3022
United States      2836
Algeria            2743
France             2693
Egypt              2479
Lebanon            2478
Name: Country, dtype: int64

Top Countries Affected By Terrorist Attacks¶

In [27]:
plt.subplots(figsize=(20,10))
sns.barplot(x=global_terror['Country'].value_counts()[:20].index, y=global_terror['Country'].value_counts()[:20].values, palette='Blues_d')
plt.title('Top Countries Affected')
plt.xlabel('Countries')
plt.ylabel('Count')
plt.xticks(rotation=90)
plt.show()

ANALYSIS ON CUSTOMIZED DATA¶

Terrorist Attacks of a Particular year and their Locations¶

Let's look at the terrorist acts in the world over a certain year.

In [30]:
import folium
from folium.plugins import MarkerCluster
In [31]:
filterYear = global_terror['Year'] == 2001
In [32]:
filterData = global_terror[filterYear] # filter data
# filterData.info()
reqFilterData = filterData.loc[:,'city':'longitude'] # get the required fields
reqFilterData = reqFilterData.dropna() # drop NaN values in latitude and longitude
reqFilterDataList = reqFilterData.values.tolist()
# reqFilterDataList
In [33]:
map = folium.Map(location = [0, 50], tiles='CartoDB positron', zoom_start=2) 
markerCluster = folium.plugins.MarkerCluster().add_to(map)
for point in range(0, len(reqFilterDataList)):
    folium.Marker(location=[reqFilterDataList[point][1],reqFilterDataList[point][2]],
                  popup = reqFilterDataList[point][0]).add_to(markerCluster)
map
Out[33]:
Make this Notebook Trusted to load map: File -> Trust Notebook

From the above map, we can depict that the maximum attacks carried out in the year 2001 was on the African Continent, almost 1325 attacks. Then, the continent South America faced the highest number of attacks, i.e. 258.

Terrorist's Origanizations Operations In Each Country¶

In [35]:
global_terror.Group.value_counts()[1:20]
Out[35]:
Taliban                                             7478
Islamic State of Iraq and the Levant (ISIL)         5613
Shining Path (SL)                                   4555
Farabundo Marti National Liberation Front (FMLN)    3351
Al-Shabaab                                          3288
New People's Army (NPA)                             2772
Irish Republican Army (IRA)                         2671
Revolutionary Armed Forces of Colombia (FARC)       2487
Boko Haram                                          2418
Kurdistan Workers' Party (PKK)                      2310
Basque Fatherland and Freedom (ETA)                 2024
Communist Party of India - Maoist (CPI-Maoist)      1878
Maoists                                             1630
Liberation Tigers of Tamil Eelam (LTTE)             1606
National Liberation Army of Colombia (ELN)          1561
Tehrik-i-Taliban Pakistan (TTP)                     1351
Palestinians                                        1125
Houthi extremists (Ansar Allah)                     1062
Al-Qaida in the Arabian Peninsula (AQAP)            1020
Name: Group, dtype: int64
In [36]:
test = global_terror[global_terror.Group.isin(['Shining Path (SL)','Taliban','Islamic State of Iraq and the Levant (ISIL)'])]
In [37]:
test.Country.unique()
Out[37]:
array(['Peru', 'Bolivia', 'Colombia', 'Argentina', 'Brazil', 'Mexico',
       'Afghanistan', 'Pakistan', 'Syria', 'Iraq', 'Turkey', 'Tunisia',
       'Lebanon', 'Turkmenistan', 'Israel', 'Belgium', 'Egypt', 'Libya',
       'Saudi Arabia', 'West Bank and Gaza Strip', 'France', 'Bahrain',
       'Jordan', 'Somalia', 'Germany', 'Yemen', 'Philippines', 'Malaysia',
       'Indonesia', 'Russia', 'Georgia', 'United Kingdom', 'Iran',
       'Australia'], dtype=object)
In [38]:
global_terror_df_group = global_terror.dropna(subset=['latitude','longitude'])
In [39]:
global_terror_df_group = global_terror_df_group.drop_duplicates(subset=['Country','Group'])
In [40]:
terrorist_groups = global_terror.Group.value_counts()[1:8].index.tolist()
global_terror_df_group = global_terror_df_group.loc[global_terror_df_group.Group.isin(terrorist_groups)]
print(global_terror_df_group.Group.unique())

["New People's Army (NPA)" 'Irish Republican Army (IRA)'
 'Shining Path (SL)' 'Farabundo Marti National Liberation Front (FMLN)'
 'Taliban' 'Al-Shabaab' 'Islamic State of Iraq and the Levant (ISIL)']
In [41]:
map = folium.Map(location=[50, 0], tiles="CartoDB positron", zoom_start=2)
markerCluster = folium.plugins.MarkerCluster().add_to(map)
for i in range(0,len(global_terror_df_group)):
    folium.Marker([global_terror_df_group.iloc[i]['latitude'],global_terror_df_group.iloc[i]['longitude']], 
                  popup='Group:{}<br>Country:{}'.format(global_terror_df_group.iloc[i]['Group'], 
                  global_terror_df_group.iloc[i]['Country'])).add_to(map)
map
Out[41]:
Make this Notebook Trusted to load map: File -> Trust Notebook
In [42]:
m1 = folium.Map(location=[50, 0], tiles="CartoDB positron", zoom_start=2)
marker_cluster = MarkerCluster(
    name='clustered icons',
    overlay=True,
    control=False,
    icon_create_function=None
)
for i in range(0,len(global_terror_df_group)):
    marker=folium.Marker([global_terror_df_group.iloc[i]['latitude'],global_terror_df_group.iloc[i]['longitude']]) 
    popup='Group:{}<br>Country:{}'.format(global_terror_df_group.iloc[i]['Group'],
                                          global_terror_df_group.iloc[i]['Country'])
    folium.Popup(popup).add_to(marker)
    marker_cluster.add_child(marker)
marker_cluster.add_to(m1)
folium.TileLayer('openstreetmap').add_to(m1)
#folium.TileLayer('Mapbox Bright').add_to(m1)
folium.TileLayer('cartodbdark_matter').add_to(m1)
folium.TileLayer('stamentoner').add_to(m1)
folium.LayerControl().add_to(m1)

m1
Out[42]:
Make this Notebook Trusted to load map: File -> Trust Notebook
In [43]:
global_terror.head()
Out[43]:
Year Month Day Country state Region city latitude longitude AttackType Killed Wounded Target Summary Group Target_type Weapon_type Motive Casualities
0 1970 7 2 Dominican Republic NaN Central America & Caribbean Santo Domingo 18.456792 -69.951164 Assassination 1 0 Julio Guzman NaN MANO-D Private Citizens & Property Unknown NaN 1
1 1970 0 0 Mexico Federal North America Mexico city 19.371887 -99.086624 Hostage Taking (Kidnapping) 0 0 Nadine Chaval, daughter NaN 23rd of September Communist League Government (Diplomatic) Unknown NaN 0
2 1970 1 0 Philippines Tarlac Southeast Asia Unknown 15.478598 120.599741 Assassination 1 0 Employee NaN Unknown Journalists & Media Unknown NaN 1
3 1970 1 0 Greece Attica Western Europe Athens 37.997490 23.762728 Bombing/Explosion 0 0 U.S. Embassy NaN Unknown Government (Diplomatic) Explosives NaN 0
4 1970 1 0 Japan Fukouka East Asia Fukouka 33.580412 130.396361 Facility/Infrastructure Attack 0 0 U.S. Consulate NaN Unknown Government (Diplomatic) Incendiary NaN 0
In [44]:
# Total Number of people killed in terror attack
killData = global_terror.loc[:,'Killed']
print('Number of people killed by terror attack:', int(sum(killData.dropna())))# drop the NaN values

Number of people killed by terror attack: 411868
In [45]:
# Let's look at what types of attacks these deaths were made of.
attackData = global_terror.loc[:,'AttackType']
# attackData
typeKillData = pd.concat([attackData, killData], axis=1)
In [46]:
typeKillData.head()
Out[46]:
AttackType Killed
0 Assassination 1
1 Hostage Taking (Kidnapping) 0
2 Assassination 1
3 Bombing/Explosion 0
4 Facility/Infrastructure Attack 0
In [47]:
typeKillFormatData = typeKillData.pivot_table(columns='AttackType', values='Killed', aggfunc='sum')
typeKillFormatData
Out[47]:
AttackType Armed Assault Assassination Bombing/Explosion Facility/Infrastructure Attack Hijacking Hostage Taking (Barricade Incident) Hostage Taking (Kidnapping) Unarmed Assault Unknown
Killed 160297 24920 157321 3642 3718 4478 24231 880 32381
In [48]:
typeKillFormatData.info()

<class 'pandas.core.frame.DataFrame'>
Index: 1 entries, Killed to Killed
Data columns (total 9 columns):
 #   Column                               Non-Null Count  Dtype
---  ------                               --------------  -----
 0   Armed Assault                        1 non-null      int32
 1   Assassination                        1 non-null      int32
 2   Bombing/Explosion                    1 non-null      int32
 3   Facility/Infrastructure Attack       1 non-null      int32
 4   Hijacking                            1 non-null      int32
 5   Hostage Taking (Barricade Incident)  1 non-null      int32
 6   Hostage Taking (Kidnapping)          1 non-null      int32
 7   Unarmed Assault                      1 non-null      int32
 8   Unknown                              1 non-null      int32
dtypes: int32(9)
memory usage: 152.0+ bytes
In [50]:
labels = typeKillFormatData.columns.tolist()
transposed = typeKillFormatData.T
values = transposed.values.flatten().tolist()  # Flatten the 2D array

fig, ax = plt.subplots(figsize=(20, 20), subplot_kw=dict(aspect="equal"))
plt.pie(values, startangle=90, autopct='%.2f%%')
plt.title('Types Of Terrorist Attacks That Cause Deaths', fontsize=30)
plt.legend(labels, loc='upper right', bbox_to_anchor=(1.3, 0.9), fontsize=15)  # Location of the legend
plt.show()
In [51]:
global_terror.head(2)
Out[51]:
Year Month Day Country state Region city latitude longitude AttackType Killed Wounded Target Summary Group Target_type Weapon_type Motive Casualities
0 1970 7 2 Dominican Republic NaN Central America & Caribbean Santo Domingo 18.456792 -69.951164 Assassination 1 0 Julio Guzman NaN MANO-D Private Citizens & Property Unknown NaN 1
1 1970 0 0 Mexico Federal North America Mexico city 19.371887 -99.086624 Hostage Taking (Kidnapping) 0 0 Nadine Chaval, daughter NaN 23rd of September Communist League Government (Diplomatic) Unknown NaN 0
In [52]:
# Number of Killed in Terrorist Attacks by Countries
countryData = global_terror.loc[:,'Country']
# countyData
countryKillData = pd.concat([countryData, killData], axis=1)
In [53]:
countryKillFormatData = countryKillData.pivot_table(columns='Country', values='Killed', aggfunc='sum')
countryKillFormatData
Out[53]:
Country Afghanistan Albania Algeria Andorra Angola Antigua and Barbuda Argentina Armenia Australia Austria ... Vietnam Wallis and Futuna West Bank and Gaza Strip West Germany (FRG) Western Sahara Yemen Yugoslavia Zaire Zambia Zimbabwe
Killed 39384 42 11066 0 3043 0 490 37 23 30 ... 1 0 1500 97 1 8776 119 324 70 154

1 rows × 205 columns

In [54]:
fig_size = plt.rcParams["figure.figsize"]
fig_size[0]=25
fig_size[1]=25
plt.rcParams["figure.figsize"] = fig_size
In [55]:
labels = countryKillFormatData.columns.tolist()
labels = labels[:50] #50 bar provides nice view
index = np.arange(len(labels))
transpoze = countryKillFormatData.T
values = transpoze.values.tolist()
values = values[:50]
values = [int(i[0]) for i in values] # convert float to int
colors = ['red', 'green', 'blue', 'purple', 'yellow', 'brown', 'black', 'gray', 'magenta', 'orange'] # color list for bar chart bar color 
fig, ax = plt.subplots(1, 1)
ax.yaxis.grid(True)
fig_size = plt.rcParams["figure.figsize"]
fig_size[0]=25
fig_size[1]=25
plt.rcParams["figure.figsize"] = fig_size
plt.bar(index, values, color = colors, width = 0.9)
plt.ylabel('Killed People', fontsize=30)
plt.xlabel('Countries', fontsize = 30)
plt.xticks(index, labels, fontsize=20, rotation=90)
plt.title('Number of People Killed By Countries', fontsize = 30)
# print(fig_size)
plt.show()
In [56]:
labels = countryKillFormatData.columns.tolist()
labels = labels[50:101]
index = np.arange(len(labels))
transpoze = countryKillFormatData.T
values = transpoze.values.tolist()
values = values[50:101]
values = [int(i[0]) for i in values]
colors = ['red', 'green', 'blue', 'purple', 'yellow', 'brown', 'black', 'gray', 'magenta', 'orange']
fig, ax = plt.subplots(1, 1)
ax.yaxis.grid(True)
fig_size = plt.rcParams["figure.figsize"]
fig_size[0]=20
fig_size[1]=20
plt.rcParams["figure.figsize"] = fig_size
plt.bar(index, values, color = colors, width = 0.9)
plt.ylabel('Killed People', fontsize=20)
plt.xlabel('Countries', fontsize = 20)
plt.xticks(index, labels, fontsize=20, rotation=90)
plt.title('Number Of People Killed By Countries', fontsize = 30)
plt.show()
In [57]:
labels = countryKillFormatData.columns.tolist()
labels = labels[101:152]
index = np.arange(len(labels))
transpoze = countryKillFormatData.T
values = transpoze.values.tolist()
values = values[101:152]
values = [int(i[0]) for i in values]
colors = ['red', 'blue', 'brown', 'orange', 'purple', 'yellow', 'black', 'green', 'gray', 'magenta']
fig, ax = plt.subplots(1, 1)
ax.yaxis.grid(True)
fig_size = plt.rcParams["figure.figsize"]
fig_size[0]=25
fig_size[1]=25
plt.rcParams["figure.figsize"] = fig_size
plt.bar(index, values, color = colors, width = 0.9)
plt.ylabel('Killed People', fontsize=20)
plt.xlabel('Countries', fontsize = 20)
plt.xticks(index, labels, fontsize=20, rotation=90)
plt.title('Number Of people Killed By Countries', fontsize = 20)
plt.show()
In [58]:
labels = countryKillFormatData.columns.tolist()
labels = labels[152:206]
index = np.arange(len(labels))
transpoze = countryKillFormatData.T
values = transpoze.values.tolist()
values = values[152:206]
values = [int(i[0]) for i in values]
colors = ['red', 'green', 'blue', 'purple', 'yellow', 'brown', 'black', 'gray', 'magenta', 'orange']
fig, ax = plt.subplots(1, 1)
ax.yaxis.grid(True)
fig_size = plt.rcParams["figure.figsize"]
fig_size[0]=25
fig_size[1]=25
plt.rcParams["figure.figsize"] = fig_size
plt.bar(index, values, color = colors, width = 0.9)
plt.ylabel('Killed People', fontsize=20)
plt.xlabel('Countries', fontsize = 20)
plt.xticks(index, labels, fontsize=20, rotation=90)
plt.title('Number Of people Killed By Countries', fontsize = 20)
plt.show()

CONCLUSIONS :¶

From the above graphs, we can see that the countries where most people are killed are : Afghanisthan, Columbia, Iran, Sri lanka, Syria, Somalia, Yemen naming a few. Even though there is a perception that Muslims are supporters of terrorism, but Muslims are the people who are most damaged by terrorist attacks.¶

So after different type of analyzation ,Overall terrorism is suddenly increased from 2010 and I have ranked the Hot zone in terms of terrorist activities in all regions -

#Middle East & North Africa¶

Iraq

Overall having highest Number of terrorist rate and sudden increase in terrorist activity after year 2010

Libya

Overall having low terrorism activity, Second Highest in terms of successful terrorist activities which takes place in this country and an increased in terrorism rate after 2010.

Yemen

Overall having low terrorism activity, Third Highest in terms of successful terrorist activities which takes place in this country and an increased in terrorism rate after 2010.

#South Asia¶

Afghanistan

Overall having 1st Highest terrorist rates and 1st highest in terms of extended terrorist rates and a very high increase in terrorism rate after 2010.

Pakistan

Overall having 2nd highest terrorist rates but there is a good decrease in terrorist rates if we consider the 20s century and an increase in terrorism rate after 2010 but not as that of increase like Afghanistan and India.

India

Overall having 3rd highest terrorist rates but if we consider extended terrorist rate so this country is 2nd highest and a very high increase in terrorism rate after 2010.

#Sub-Saharan Africa¶

Nigeria

Overall having low terrorism activity but 1st Highest in terms of extended terrorist activities which takes place in this country and 2nd in terms of increased of terrorism rate after 2010.

Somalia

Overall having low terrorism activity but 2nd Highest in terms of extended terrorist activities which takes place in this country and 1st in terms of increased of terrorism rate after 2010.

Sudan

Overall having low terrorism activity but 3rd Highest in terms of extended terrorist activities which takes place in this country and a bit increase in terrorism rate after 2010.

#Solution¶

- More security surveillance required at Iraq.

- Noticing the trends of terrorism activities , hugely populated regions suffer major kill ratios. This must be controlled with strict border rules

- Impose strict rules in controlling the explosions produce worldwide limiting the number of supplies #High alert at densly populated locations in each city as it is noticed that terrorists target is private citizens and property

Thank You!¶

GitHub: https://github.com/anujtiwari21?tab=repositories¶